The measurement of perfusion and filtration of blood in biological tissue give rise to important clinical parameters used in diagnosis, follow-up, and therapy. In this paper, we address techniques for perfusion analysis using processed contrast agent concentration data from dynamic MRI acquisitions. A new methodology for analysis is evaluated and verified using synthetic data generated on a tissue geometry.

Abstract Better understanding of the complex processes driving tumor growth and metastases is critical for developing targeted treatment strategies in cancer. Radiomics extracts large amounts of features from medical images which enables radiomic tumor profiling in combination with clinical markers. However, analyzing complex imaging data in combination with clinical data is not trivial and supporting tools aiding in these exploratory analyses are presently missing. In this paper, we present an approach that aims to enable the analysis of multiparametric medical imaging data in combination with numerical, ordinal, and categorical clinical parameters to validate established and unravel novel biomarkers. We propose a hybrid approach where dimensionality reduction to a single axis is combined with multiple linked views allowing clinical experts to formulate hypotheses based on all available imaging data and clinical parameters. This may help to reveal novel tumor characteristics in relation to molecular targets for treatment, thus providing better tools for enabling more personalized targeted treatment strategies. To confirm the utility of our approach, we closely collaborate with experts from the field of gynecological cancer imaging and conducted an evaluation with six experts in this field.

A large variety of severe medical conditions involve alterations in microvascular circulation. Hence, measurements or simulation of circulation and perfusion has considerable clinical value and can be used for diagnostics, evaluation of treatment efficacy, and for surgical planning. However, the accuracy of traditional tracer kinetic one-compartment models is limited due to scale dependency. As a remedy, we propose a scale invariant mathematical framework for simulating whole brain perfusion. The suggested framework is based on a segmentation of anatomical geometry down to imaging voxel resolution. Large vessels in the arterial and venous network are identified from time-of-flight (ToF) and quantitative susceptibility mapping (QSM). Macro-scale flow in the large-vessel-network is accurately modelled using the Hagen-Poiseuille equation, whereas capillary flow is treated as two-compartment porous media flow. Macro-scale flow is coupled with micro-scale flow by a spatially distributing support function in the terminal endings. Perfusion is defined as the transition of fluid from the arterial to the venous compartment. We demonstrate a whole brain simulation of tracer propagation on a realistic geometric model of the human brain, where the model comprises distinct areas of grey and white matter, as well as large vessels in the arterial and venous vascular network. Our proposed framework is an accurate and viable alternative to traditional compartment models, with high relevance for simulation of brain perfusion and also for restoration of field parameters in clinical brain perfusion applications.

Watershed segmentation is useful for a number of image segmentation problems with a wide range of practical applications. Traditionally, the tracking of the immersion front is done by applying a fast sorting algorithm. In this work, we explore a continuous approach based on a geometric description of the immersion front which gives rise to a partial differential equation. The main advantage of using a partial differential equation to track the immersion front is that the method becomes versatile and may easily be stabilized by introducing regularization terms. Coupling the geometric approach with a proper "merging strategy" creates a robust algorithm which minimizes over- and under-segmentation even without predefined markers. Since reliable markers defined prior to segmentation can be difficult to construct automatically for various reasons, being able to treat marker-free situations is a major advantage of the proposed method over earlier watershed formulations. The motivation for the methods developed in this paper is taken from high-throughput screening of cells. A fully automated segmentation of single cells enables the extraction of cell properties from large data sets, which can provide substantial insight into a biological model system. Applying smoothing to the boundaries can improve the accuracy in many image analysis tasks requiring a precise delineation of the plasma membrane of the cell. The proposed segmentation method is applied to real images containing fluorescently labeled cells, and the experimental results show that our implementation is robust and reliable for a variety of challenging segmentation tasks.

Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) of the kidneys requires proper motion correction and segmentation to enable an estimation of glomerular filtration rate through pharmacokinetic modeling. Traditionally, co-registration, segmentation, and pharmacokinetic modeling have been applied sequentially as separate processing steps. In this paper, a combined 4D model for simultaneous registration and segmentation of the whole kidney is presented. To demonstrate the model in numerical experiments, we used normalized gradients as data term in the registration and a Mahalanobis distance from the time courses of the segmented regions to a training set for supervised segmentation. By applying this framework to an input consisting of 4D image time series, we conduct simultaneous motion correction and two-region segmentation into kidney and background. The potential of the new approach is demonstrated on real DCE-MRI data from ten healthy volunteers.

This work presents a unified framework for whole cell segmentation of surface stained living cells from 3-D data sets of fluorescent images. Every step of the process is described, image acquisition, prefiltering, ridge enhancement, cell segmentation, and a segmentation evaluation. The segmentation results from two different automated approaches for segmentation are compared to manual segmentation of the same data using a rigorous evaluation scheme. This revealed that combination of the respective cell types with the most suitable microscopy method resulted in high success rates up to 97%. The described approach permits to automatically perform a statistical analysis of various parameters from living cells.